云存储副本优化选择策略

Optimization Selection Strategy of Cloud Storage Replica

为了提高云计算环境中系统的整体数据调度效率,对云存储系统中的副本选择问题进行研究,提出一种基于蚁群觅食原理的云存储副本优化选择策略。该策略利用蚁群算法在解决优化问题上的优势,将自然环境中蚁群的觅食过程与云存储中的副本选择过程相结合;再充分应用信息素的动态变化规律以及高斯概率分布特性优化副本的选择方式,得出一组副本资源的最优解,从而为数据请求响应合适的副本。在OptorSim仿真平台上对该算法进行实现,实验结果表明该算法具有不错的表现,如在平均作业用时这一性能指标上相比原始蚁群算法提升了18.7%,从而在一定程度上减少了副本选择过程的时间消耗,降低了网络负载。

In order to improve the efficiency of the overall data scheduling in the cloud computing environment and research the copy selection problem in the cloud storage system,an optimal selection strategy of cloud storage replicas based on ant colony feeding principle was proposed.In view of the advantages of ant colony algorithm in solving the optimization problem,this strategy combines the ant colony feeding process in the natural environment with the replica selection process in the cloud storage.Furthermore,the pheromone dynamic change law and the Gaussian probability distribution characteristic are used to optimize the replica selection method,so as to obtain the optimal solution of a set of replica resources,and then respond to the appropriate replica of the data request.The experimental results show that the algorithm has good performance in the OptorSim simulation platform.For example,the average operation time is 18.7%higher than that of the original ant colony algorithm,and the time consumption of copy selection is reduced to a certain extent,thus reducing network load.